Electrome alterations in a plant-pathogen system: Toward early diagnosis

Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome

Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant's electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus. This study aimed to characterize the electrome of common bean (Phaseolus vulgaris L.) cv. BRS-Expedito, subjected to different water availabilities, seeking patterns linked to these stimuli. For this purpose, bean plants in the vegetative stage were subjected to the following treatments: (I) distilled water; (II) half-strength Hoagland's nutrient solution; (III) -2 MPa PEG solution; and (IV) -2 MPa NaCl solution. Electrical signals were recorded within a Faraday's cage using the MP36 electronic system for data acquisition. Concurrently, plant water status was assessed by monitoring leaf turgor variation. Leaf temperature was additionally measured. Various analyses were conducted on the electrical time series data, including arithmetic average of voltage variation, skewness, kurtosis, Probability Density Function (PDF), autocorrelation, Power Spectral Density (PSD), Approximate Entropy (ApEn), Fast Fourier Transform (FFT), and Multiscale Approximate Entropy (ApEn(s)). Statistical analyses were performed on leaf temperature, voltage variation, skewness, kurtosis, PDF µ exponent, autocorrelation, PSD β exponent, and approximate entropy data. Machine Learning analyses were applied to identify classifiable patterns in the electrical time series. Characterization of the electrome of BRS-Expedito beans revealed stimulus-dependent profiles, even when alterations in water availability stimuli were similar in terms of quality and intensity. Additionally, it was observed that the bean electrome exhibits high levels of complexity, which are altered by different stimuli, with more intense and aversive stimuli leading to drastic reductions in complexity levels. Notably, one of the significant findings was the 100% accuracy of Small Vector Machine in detecting salt stress using electrome data. Furthermore, the study highlighted alterations in the plant electrome under low water potential before observable leaf turgor changes. This work demonstrates the potential use of the electrome as a physiological indicator of the water status in bean plants.

Approximate entropy: a promising tool to understand the hidden electrical activity of fruit

Fruits, like other parts of the plant, appear to have a rich electrical activity that may contain information. Here, we present data showing differences in the electrome complexity of tomato fruits through ripening and discuss possible physiological processes involved. The complexity of the signals, measured through approximate entropy, varied along the fruit ripening process. When analyzing the fruits individually, a decrease in entropy values was observed when they entered the breaker stage, followed by a tendency to increase again when they entered the light red stage. Consequently, the data obtained showed a decrease in signal complexity in the breaker stage, probably due to some physiological process that ends up predominating to the detriment of others. This result may be linked to processes involved in ripening, such as climacteric. Electrophysiological studies in the reproductive stage of the plant are still scarce, and research in this direction is of paramount importance to understand whether the electrical signals observed can transmit information from reproductive structures to other modules of plants. This work opens the possibility of studying the relationship between the electrical activity and fruit ripening through the analysis of approximate entropy. More studies are necessary to understand whether there is a correlation or a cause-response relationship in the phenomena involved. There is a myriad of possibilities for the applicability of this knowledge to different areas, from understanding the cognitive processes of plants to achieving more accurate and sustainable agriculture.

Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data

Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification of the raw data and introduce a high computational cost. The Deep Learning (DL) techniques automatically learn the classification targets from the input data, overcoming the need for precalculated features. However, they are scarcely explored for identifying plant stress on electrophysiological recordings. This study applies DL techniques to the raw electrophysiological data from 16 tomato plants growing in typical production conditions to detect the presence of stress caused by a nitrogen deficiency. The proposed approach predicts the stressed state with an accuracy of around 88%, which could be increased to over 96% using a combination of the obtained prediction confidences. It outperforms the current state-of-the-art with over 8% higher accuracy and a potential for a direct application in production conditions. Moreover, the proposed approach demonstrates the ability to detect the presence of stress at its early stage. Overall, the presented findings suggest new means to automatize and improve agricultural practices with the aim of sustainability.

The Electrome of a Parasitic Plant in a Putative State of Attention Increases the Energy of Low Band Frequency Waves: A Comparative Study with Neural Systems

Selective attention is an important cognitive phenomenon that allows organisms to flexibly engage with certain environmental cues or activities while ignoring others, permitting optimal behaviour. It has been proposed that selective attention can be present in many different animal species and, more recently, in plants. The phenomenon of attention in plants would be reflected in its electrophysiological activity, possibly being observable through electrophytographic (EPG) techniques. Former EPG time series obtained from the parasitic plant Cuscuta racemosa in a putative state of attention towards two different potential hosts, the suitable bean (Phaseolus vulgaris) and the unsuitable wheat (Triticum aestivum), were revisited. Here, we investigated the potential existence of different band frequencies (including low, delta, theta, mu, alpha, beta, and gamma waves) using a protocol adapted from neuroscientific research. Average band power (ABP) was used to analyse the energy distribution of each band frequency in the EPG signals, and time dispersion analysis of features (TDAF) was used to explore the variations in the energy of each band. Our findings indicated that most band waves were centred in the lower frequencies. We also observed that C. racemosa invested more energy in these low-frequency waves when suitable hosts were present. However, we also noted peaks of energy investment in all the band frequencies, which may be linked to extremely low oscillatory electrical signals in the entire tissue. Overall, the presence of suitable hosts induced a higher energy power, which supports the hypothesis of attention in plants. We further discuss and compare our results with generic neural systems.

Systemic Signals Induced by Single and Combined Abiotic Stimuli in Common Bean Plants

To survive in a dynamic environment growing fixed to the ground, plants have developed mechanisms for monitoring and perceiving the environment. When a stimulus is perceived, a series of signals are induced and can propagate away from the stimulated site. Three distinct types of systemic signaling exist, i.e., (i) electrical, (ii) hydraulic, and (iii) chemical, which differ not only in their nature but also in their propagation speed. Naturally, plants suffer influences from two or more stimuli (biotic and/or abiotic). Stimuli combination can promote the activation of new signaling mechanisms that are explicitly activated, as well as the emergence of a new response. This study evaluated the behavior of electrical (electrome) and hydraulic signals after applying simple and combined stimuli in common bean plants. We used simple and mixed stimuli applications to identify biochemical responses and extract information from the electrical and hydraulic patterns. Time series analysis, comparing the conditions before and after the stimuli and the oxidative responses at local and systemic levels, detected changes in electrome and hydraulic signal profiles. Changes in electrome are different between types of stimulation, including their combination, and systemic changes in hydraulic and oxidative dynamics accompany these electrical signals.

Local action of moderate heating and illumination induces propagation of hyperpolarization electrical signals in wheat plants

Electrical signals (ESs), which are generated in irritated zones of plants and propagate into their non-irritated parts, are hypothesized to be an important mechanism of a plant systemic response on the local action of adverse factors. This hypothesis is supported by influence of ESs on numerous physiological processes including expression of defense genes, production of stress phytohormones, changes in photosynthetic processes and transpiration, stimulation of respiration and others. However, there are several questions, which require solution to support the hypothesis. Particularly, the non-physiological stimuli (e.g., strong heating or burning) are often used for induction of ESs; in contrast, the ES induction under action of physiological stressors with moderate intensities requires additional investigations. Influence of long-term environmental factors on generation and propagation of ESs is also weakly investigated. In the current work, we investigated ESs induced by local action of the moderate heating and illumination in wheat plants under irrigated and drought conditions. It was shown that combination of the moderate heating (40°C) and illumination (blue light, 540 μmol m−2s−1) induced electrical signals which were mainly depolarization electrical signals near the irritation zone and hyperpolarization electrical signals (HESs) on the distance from this zone. The moderate soil drought did not influence HESs; in contrast, the strong soil drought significantly decreased amplitude of HESs. Finally, it was shown that the moderate heating could induce HESs without additional action of illumination. It was hypothesized that both hyperpolarization and depolarization ESs could be caused by the hydraulic wave.

Hyperpolarization electrical signals induced by local action of moderate heating influence photosynthetic light reactions in wheat plants

Local action of stressors induces fast changes in physiological processes in intact parts of plants including photosynthetic inactivation. This response is mediated by generation and propagation of depolarization electrical signals (action potentials and variation potentials) and participates in increasing plant tolerance to action of adverse factors. Earlier, we showed that a local action of physiological stimuli (moderate heating and blue light), which can be observed under environmental conditions, induces hyperpolarization electrical signals (system potentials) in wheat plants. It potentially means that these signals can play a key role in induction of fast physiological changes under the local action of environmental stressors. The current work was devoted to investigation of influence of hyperpolarization electrical signals induced by the local action of the moderate heating and blue light on parameters of photosynthetic light reactions. A quantum yield of photosystem II (Ф_PSII) and a non-photochemical quenching of chlorophyll fluorescence (NPQ) in wheat plants were investigated. It was shown that combination of the moderate heating (40°C) and blue light (540 µmol m^-2s^-1) decreased Ф_PSII and increased NPQ; these changes were observed in 3-5 cm from border of the irritated zone and dependent on intensity of actinic light. The moderate soil drought (7 days) increased magnitude of photosynthetic changes and shifted their localization which were observed on 5-7 cm from the irritated zone; in contrast, the strong soil drought (14 days) suppressed these changes. The local moderate heating decreased Ф_PSII and increased NPQ without action of the blue light; in contrast, the local blue light action without heating weakly influenced these parameters. It meant that just local heating was mechanism of induction of the photosynthetic changes. Finally, propagation of hyperpolarization electrical signals (system potentials) was necessary for decreasing Ф_PSII and increasing NPQ. Thus, our results show that hyperpolarization electrical signals induced by the local action of the moderate heating inactivates photosynthetic light reactions; this response is similar with photosynthetic changes induced by depolarization electrical signals. The soil drought and actinic light intensity can influence parameters of these photosynthetic changes.

Do plants pay attention? A possible phenomenological-empirical approach

Attention is the important ability of flexibly controlling limited cognitive resources. It ensures that organisms engage with the activities and stimuli that are relevant to their survival. Despite the cognitive capabilities of plants and their complex behavioural repertoire, the study of attention in plants has been largely neglected. In this article, we advance the hypothesis that plants are endowed with the ability of attaining attentive states. We depart from a transdisciplinary basis of philosophy, psychology, physics and plant ecophysiology to propose a framework that seeks to explain how plant attention might operate and how it could be studied empirically. In particular, the phenomenological approach seems particularly important to explain plant attention theoretically, and plant electrophysiology seems particularly suited to study it empirically. We propose the use of electrophysiological techniques as a viable way for studying it, and we revisit previous work to support our hypothesis. We conclude this essay with some remarks on future directions for the study of plant attention and its implications to botany.

Impedance Measures for Detecting Electrical Responses during Acute Injury and Exposure of Compounds to Roots of Plants

Electrical activity is widely used for assessing a plant's response to an injury or environmental stimulus. Commonly, a differential electrode recording between silver wire leads with the reference wire connected to the soil, or a part of the plant, is used. One method uses KCl-filled glass electrodes placed into the plant, similar to recording membrane/cell potentials in animal tissues. This method is more susceptible to artifacts of equipment noise and photoelectric effects than an impedance measure. An impedance measure using stainless steel wires is not as susceptible to electrically induced noises. Impedance measurements are able to detect injury in plants as well as exposure of the roots to environmental compounds (glutamate). The impedance measures were performed in 5 different plants (tomato, eggplant, pepper, liverwort, and Coleus scutellarioides), and responses to mechanical movement of the plant, as well as injury, were recorded. Monitoring electrical activity in a plant that arises in a distant plant was also demonstrated using the impedance method. The purpose of this report is to illustrate the ease in using impedance measures for monitoring electrical signals from individual plants or aggregates of plants for potentially scaling for high throughput and monitoring controlled culturing and outdoor field environments.

Influence of Burning-Induced Electrical Signals on Photosynthesis in Pea Can Be Modified by Soil Water Shortage

Local damage to plants can induce fast systemic physiological changes through generation and propagation of electrical signals. It is known that electrical signals influence numerous physiological processes including photosynthesis; an increased plant tolerance to actions of stressors is a result of these changes. It is probable that parameters of electrical signals and fast physiological changes induced by these signals can be modified by the long-term actions of stressors; however, this question has been little investigated. Our work was devoted to the investigation of the parameters of burning-induced electrical signals and their influence on photosynthesis under soil water shortage in pea seedlings. We showed that soil water shortage decreased the amplitudes of the burning-induced depolarization signals (variation potential) and the magnitudes of photosynthetic inactivation (decreasing photosynthetic CO₂ assimilation and linear electron flow and increasing non-photochemical quenching of the chlorophyll fluorescence and cyclic electron flow around photosystem I) caused by these signals. Moreover, burning-induced hyperpolarization signals (maybe, system potentials) and increased photosynthetic CO₂ assimilation could be observed under strong water shortage. It was shown that the electrical signal-induced increase of the leaf stomatal conductance was a potential mechanism for the burning-induced activation of photosynthetic CO₂ assimilation under strong water shortage; this mechanism was not crucial for photosynthetic response under control conditions or weak water shortage. Thus, our results show that soil water shortage can strongly modify damage-induced electrical signals and fast physiological responses induced by these signals.

Machine Learning for Automatic Classification of Tomato Ripening Stages Using Electrophysiological Recordings

The physiological processes underlying fruit ripening can lead to different electrical signatures at each ripening stage, making it possible to classify tomato fruit through the analysis of electrical signals. Here, the electrical activity of tomato fruit (Solanum lycopersicum var. cerasiforme) during ripening was investigated as tissue voltage variations, and Machine Learning (ML) techniques were used for the classification of different ripening stages. Tomato fruit was harvested at the mature green stage and placed in a Faraday's cage under laboratory-controlled conditions. Two electrodes per fruit were inserted 1 cm apart from each other. The measures were carried out continuously until the entire fruits reached the light red stage. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. Descriptive analysis from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis). Finally, ApEn, PCA1, PCA2, and PCA3 were obtained. These features were used in ML analyses for looking for classifiable patterns of the three different ripening stages: mature green, breaker, and light red. The results showed that it is possible to classify the ripening stages using the fruit's electrical activity. It was also observed, using precision, sensitivity, and F1-score techniques, that the breaker stage was the most classifiable among all stages. It was found a more accurate distinction between mature green × breaker than between breaker × light red. The ML techniques used seem to be a novel tool for classifying ripening stages. The features obtained from electrophysiological time series have the potential to be used for supervised training, being able to help in more accurate classification of fruit ripening stages.

Electrical Signals, Plant Tolerance to Actions of Stressors, and Programmed Cell Death: Is Interaction Possible?

In environmental conditions, plants are affected by abiotic and biotic stressors which can be heterogenous. This means that the systemic plant adaptive responses on their actions require long-distance stress signals including electrical signals (ESs). ESs are based on transient changes in the activities of ion channels and H⁺-ATP-ase in the plasma membrane. They influence numerous physiological processes, including gene expression, phytohormone synthesis, photosynthesis, respiration, phloem mass flow, ATP content, and many others. It is considered that these changes increase plant tolerance to the action of stressors; the effect can be related to stimulation of damages of specific molecular structures. In this review, we hypothesize that programmed cell death (PCD) in plant cells can be interconnected with ESs. There are the following points supporting this hypothesis. (i) Propagation of ESs can be related to ROS waves; these waves are a probable mechanism of PCD initiation. (ii) ESs induce the inactivation of photosynthetic dark reactions and activation of respiration. Both responses can also produce ROS and, probably, induce PCD. (iii) ESs stimulate the synthesis of stress phytohormones (e.g., jasmonic acid, salicylic acid, and ethylene) which are known to contribute to the induction of PCD. (iv) Generation of ESs accompanies K⁺ efflux from the cytoplasm that is also a mechanism of induction of PCD. Our review argues for the possibility of PCD induction by electrical signals and shows some directions of future investigations in the field.

Stochastic Spatial Heterogeneity in Activities of H+-ATP-Ases in Electrically Connected Plant Cells Decreases Threshold for Cooling-Induced Electrical Responses

H⁺-ATP-ases, which support proton efflux through the plasma membrane, are key molecular transporters for electrogenesis in cells of higher plants. Initial activities of the transporters can influence the thresholds of generation of electrical responses induced by stressors and modify other parameters of these responses. Previously, it was theoretically shown that the stochastic heterogeneity of individual cell thresholds for electrical responses in a system of electrically connected neuronal cells can decrease the total threshold of the system (“diversity-induced resonance”, DIR). In the current work, we tested a hypothesis about decreasing the thresholds of generation of cooling-induced electrical responses in a system of electrically connected plant cells with increasing stochastic spatial heterogeny in the initial activities of H⁺-ATP-ases in these cells. A two-dimensional model of the system of electrically connected excitable cells (simple imitation of plant leaf), which was based on a model previously developed in our works, was used for the present investigation. Simulation showed that increasing dispersion in the distribution of initial activities of H⁺-ATP-ases between cells decreased the thresholds of generation of cooling-induced electrical responses. In addition, the increasing weakly influenced the amplitudes of electrical responses. Additional analysis showed two different mechanisms of the revealed effect. The increasing spatial heterogeneity in activities of H⁺-ATP-ases induced a weak positive shift of the membrane potential at rest. The shift decreased the threshold of electrical response generation. However, the decreased threshold induced by increasing the H⁺-ATP-ase activity heterogeneity was also observed after the elimination of the positive shift. The result showed that the “DIR-like” mechanism also participated in the revealed effect. Finally, we showed that the standard deviation of the membrane potentials before the induction of action potentials could be used for the estimation of thresholds of cooling-induced plant electrical responses. Thus, spatial heterogeneity in the initial activities of H⁺-ATP-ases can be a new regulatory mechanism influencing the generation of electrical responses in plants under actions of stressors.

Fruit Herbivory Alters Plant Electrome: Evidence for Fruit-Shoot Long-Distance Electrical Signaling in Tomato Plants

The electrical activity of tomato plants subjected to fruit herbivory was investigated. The study aimed to test the hypothesis that tomato fruits transmit long-distance electrical signals to the shoot when subjected to herbivory. For such, time series classification by machine learning techniques and analyses related to the oxidative response were employed. Tomato plants (cv. “Micro-Tom”) were placed into a Faraday's cage and an electrode pair was inserted in the fruit's peduncle. Helicoverpa armigera caterpillars were placed on the fruit (either green and ripe) for 24 h. The time series were recorded before and after the fruit's exposure of the caterpillars. The plant material for chemical analyses was collected 24 and 48 h after the end of the acquisition of electrophysiological data. The time series were analyzed by the following techniques: Fast Fourier Transform (FFT), Wavelet Transform, Power Spectral Density (PSD), and Approximate Entropy. The following features from FFT, PSD, and Wavelet Transform were used for PCA (Principal Component Analysis): average, maximum and minimum value, variance, skewness, and kurtosis. Additionally, these features were used in Machine Learning (ML) analyses for looking for classifiable patterns between tomato plants before and after fruit herbivory. Also, we compared the electrome before and after herbivory in the green and ripe fruits. To evaluate an oxidative response in different organs, hydrogen peroxide, superoxide anion, catalase, ascorbate peroxidase, guaiacol peroxidase, and superoxide dismutase activity were evaluated in fruit and leaves. The results show with 90% of accuracy that the electrome registered in the fruit's peduncle before herbivory is different from the electrome during predation on the fruits. Interestingly, there was also a sharp difference in the electrome of the green and ripe fruits' peduncles before, but not during, the herbivory, which demonstrates that the signals generated by the herbivory stand over the others. Biochemical analysis showed that herbivory in the fruit triggered an oxidative response in other parts of the plant. Here, we demonstrate that the fruit perceives biotic stimuli and transmits electrical signals to the shoot of tomato plants. This study raises new possibilities for studies involving electrical signals in signaling and systemic response, as well as for the applicability of ML to classify electrophysiological data and its use in early diagnosis.

Identifying General Stress in Commercial Tomatoes Based on Machine Learning Applied to Plant Electrophysiology

Automated monitoring of plant health is becoming a crucial component for optimizing agricultural production. Recently, several studies have shown that plant electrophysiology could be used as a tool to determine plant status related to applied stressors. However, to the best of our knowledge, there have been no studies relating electrical plant response to general stress responses as a proxy for plant health. This study models general stress of plants exposed to either biotic or abiotic stressors, namely drought, nutrient deficiencies or infestation with spider mites, using electrophysiological signals acquired from 36 plants. Moreover, in the signal processing procedure, the proposed workflow reuses information from the previous steps, therefore considerably reducing computation time regarding recent related approaches in the literature. Careful choice of the principal parameters leads to a classification of the general stress in plants with more than 80% accuracy. The main descriptive statistics measured together with the Hjorth complexity provide the most discriminative information for such classification. The presented findings open new paths to explore for improved monitoring of plant health.

Influence of Local Burning on Difference Reflectance Indices Based on 400-700 nm Wavelengths in Leaves of Pea Seedlings

Local damage (e.g., burning) induces a variation potential (VP), which is an important electrical signal in higher plants. A VP propagates into undamaged parts of the plant and influences numerous physiological processes, including photosynthesis. Rapidly increasing plant tolerance to stressors is likely to be a result of the physiological changes. Thus, developing methods of revealing VP-induced physiological changes can be used for the remote sensing of plant systemic responses to local damage. Previously, we showed that burning-induced VP influenced a photochemical reflectance index in pea leaves, but the influence of the electrical signals on other reflectance indices was not investigated. In this study, we performed a complex analysis of the influence of VP induction by local burning on difference reflectance indices based on 400–700 nm wavelengths in leaves of pea seedlings. Heat maps of the significance of local burning-induced changes in the reflectance indices and their correlations with photosynthetic parameters were constructed. Large spectral regions with significant changes in these indices after VP induction were revealed. Most changes were strongly correlated to photosynthetic parameters. Some indices, which can be potentially effective for revealing local burning-induced photosynthetic changes, are separately shown. Our results show that difference reflectance indices based on 400–700 nm wavelengths can potentially be used for the remote sensing of plant systemic responses induced by local damages and subsequent propagation of VPs.

Friends, neighbours and enemies: an overview of the communal and social biology of plants

Plants were traditionally seen as rather passive actors in their environment, interacting with each other only in so far as they competed for the same resources. In the last 30 years, this view has been spectacularly overturned, with a wealth of evidence showing that plants actively detect and respond to their neighbours. Moreover, there is evidence that these responses depend on the identity of the neighbour, and that plants may cooperate with their kin, displaying social behaviour as complex as that observed in animals. These plant-plant interactions play a vital role in shaping natural ecosystems, and are also very important in determining agricultural productivity. However, in terms of mechanistic understanding, we have only just begun to scratch the surface, and many aspects of plant-plant interactions remain poorly understood. In this review, we aim to provide an overview of the field of plant-plant interactions, covering the communal interactions of plants with their neighbours as well as the social behaviour of plants towards their kin, and the consequences of these interactions. We particularly focus on the mechanisms that underpin neighbour detection and response, highlighting both progress and gaps in our understanding of these fascinating but previously overlooked interactions.

Social networking in crop plants: Wired and wireless cross-plant communications

The plant-associated microbial community (microbiome) has an important role in plant-plant communications. Plants decipher their complex habitat situations by sensing the environmental stimuli and molecular patterns and associated with microbes, herbivores and dangers. Perception of these cues generates inter/intracellular signals that induce modifications of plant metabolism and physiology. Signals can also be transferred between plants via different mechanisms, which we classify as wired- and wireless communications. Wired communications involve direct signal transfers between plants mediated by mycorrhizal hyphae and parasitic plant stems. Wireless communications involve plant volatile emissions and root exudates elicited by microbes/insects, which enable inter-plant signalling without physical contact. These producer-plant signals induce microbiome adaptation in receiver plants via facilitative or competitive mechanisms. Receiver plants eavesdrop to anticipate responses to improve fitness against stresses. An emerging body of information in plant-plant communication can be leveraged to improve integrated crop management under field conditions.

Classification of Plant Electrophysiology Signals for Detection of Spider Mites Infestation in Tomatoes

Herbivorous arthropods, such as spider mites, are one of the major causes of annual crop losses. They are usually hard to spot before a severe infestation takes place. When feeding, these insects cause external perturbation that triggers changes in the underlying physiological process of a plant, which are expressed by a generation of distinct variations of electrical potential. Therefore, plant electrophysiology data portray information of the plant state. Analyses involving machine learning techniques applied to plant electrical response triggered by spider mite infestation have not been previously reported. This study investigates plant electrophysiological signals recorded from 12 commercial tomatoes plants contaminated with spider mites and proposes a workflow based on Gradient Boosted Tree algorithm for an automated differentiation of the plant’s normal state from the stressed state caused by infestation. The classification model built using the signal samples recorded during daylight and employing a reduced feature subset performs with an accuracy of 80% in identifying the plant’s stressed state. Furthermore, the Hjorth complexity encloses the most relevant information for discrimination of the plant status. The obtained findings open novel access towards automated detection of insect infestation in greenhouse crops and, consequently, more optimal prevention and treatment approaches.

Detection of Different Hosts From a Distance Alters the Behaviour and Bioelectrical Activity of Cuscuta racemosa

In our study, we investigated some physiological and ecological aspects of the life of Cuscuta racemosa Mart. (Convolvulaceae) plants with the hypothesis that they recognise different hosts at a distance from them, and they change their survival strategy depending on what they detect. We also hypothesised that, as an attempt of prolonging their survival through photosynthesis, the synthesis of chlorophylls (a phenomenon not completely explained in these parasitic plants) would be increased if the plants don't detect a host. We quantified the pigments related to photosynthesis in different treatments and employed techniques such as electrophysiological time series recording, analyses of the complexity of the obtained signals, and machine learning classification to test our hypotheses. The results demonstrate that the absence of a host increases the amounts of chlorophyll a, chlorophyll b, and β-carotene in these plants, and the content varied depending on the host presented. Besides, the electrical signalling of dodders changes according to the species of host perceived in patterns detectable by machine learning techniques, suggesting that they recognise from a distance different host species. Our results indicate that electrical signalling might underpin important processes such as foraging in plants. Finally, we found evidence for a likely process of attention in the dodders toward the host plants. This is probably to be the first empirical evidence for attention in plants and has important implications on plant cognition studies.